Second, to efficiently find the superior combination of G and D, we choose the evolutionary algorithm (EA) based NAS method based on the following considerations. 1) EA is a global optimization method and robust to initial states [1]}. According to [2]}, [3]}, EA-based NAS usually achieves better results than other types of NAS methods on the image classification tasks. However, the search cost of EA-based methods is much expensive, e.g., [2]} takes 3,150 GPU days to search on the CIFAR-10 dataset. To alleviate resource consumption, we adopt the weight-sharing strategy [5]} and the low-fidelity method [6]} to reduce the search time on the CIFAR-10 dataset to 1.2 GPU days. 2) EA is good at solving multi-objective optimization problems. Since there is currently no consensus as to which metric best reflects the performance of GANs [7]}, it is necessary to consider multiple conflicting metrics, such as Inception Score (IS) [8]} that measures the quality and diversity of the generated images, and Fréchet Inception Distance (FID) [9]} that captures the similarity between the generated images and real images. As summarized in Tab. REF , GD-based NAS-GAN optimize architectures via loss value, which cannot reflect the status of GANs [10]}, [11]}. COEGAN [12]} is also an EA-based NAS-GAN method. It uses adversarial loss value to monitor discriminator performance, but the results evidence that loss value cannot ensure a proper evolution of the architectures. Although E2GAN [13]} and EGAN [14]} use a linear combination of metrics for evaluation, they introduce an additional ratio coefficient, the optimal value of which is hard to find, thus increasing searching difficulty. To this end, we adopt a widely used selection method, non-dominated sorting strategy [15]}, to produce the Pareto-front architectures under three different objectives: IS, FID, and model size.
